Medium- to long-term nickel price forecasting using LSTM and GRU networks

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dc.authoridOzdemir, Ali Can/0000-0003-3064-4264
dc.authoridZor, Kasim/0000-0001-6443-114X
dc.contributor.authorOzdemir, Ali Can
dc.contributor.authorBulus, Kurtulus
dc.contributor.authorZor, Kasim
dc.date.accessioned2025-01-06T17:43:47Z
dc.date.available2025-01-06T17:43:47Z
dc.date.issued2022
dc.description.abstractRecently, nickel is a critical metal for manufacturing stainless steel, rechargeable electric vehicle batteries, and alloys utilized in the state-of-the-art technologies. The use of more environmentally friendly electric vehicles has become widespread and brought tackling climate change to forefront, especially for reducing greenhouse gas emissions. Therefore, the demand for rechargeable batteries that power electric vehicles and the need for the nickel in the production of these batteries will increase as well. In addition to those, nickel prices significantly impact mine investment decisions, mine planning, economic development of nickel companies, and countries that depend on nickel resources. However, there is uncertainty about how the nickel price will trend in the future, and the solution to this problem attracts the attention of researchers. For forecasting nickel price, this paper proposes recurrent neural networks-based on long short-term memory (LSTM) and gated recurrent unit (GRU) networks, classified as deep learning algorithms. Mean absolute percentage error (MAPE) was used as the performance measure to compute the accuracy of the proposed techniques. As a result, it has been determined that the LSTM and GRU networks are very useful and successful in forecasting the nickel price variations owing to having average MAPE values of 7.060% and 6.986%, respectively. Furthermore, it has been observed that GRU networks surpassed the LSTM networks by 33% in terms of average computational time.
dc.description.sponsorshipScientific Project Unit of ?; ukurova University [FBA-2019-11998]; Science and Technology University [21103013]
dc.description.sponsorshipFunding sources This work was supported by the Scientific Project Unit of ?ukurova University [grant number FBA-2019-11998] and by the Scientific Proj-ect Unit of Adana Alparslan T?rkes? Science and Technology University [grant number 21103013] .
dc.identifier.doi10.1016/j.resourpol.2022.102906
dc.identifier.issn0301-4207
dc.identifier.issn1873-7641
dc.identifier.scopus2-s2.0-85135516131
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1016/j.resourpol.2022.102906
dc.identifier.urihttps://hdl.handle.net/20.500.14669/2798
dc.identifier.volume78
dc.identifier.wosWOS:000865022900005
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherElsevier Sci Ltd
dc.relation.ispartofResources Policy
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241211
dc.subjectNickel price forecasting
dc.subjectLSTM networks
dc.subjectGRU networks
dc.subjectRecurrent neural networks
dc.subjectDeep learning
dc.titleMedium- to long-term nickel price forecasting using LSTM and GRU networks
dc.typeArticle

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